Identifying Spatial Variation Patterns in Multivariate Manufacturing Processes

Large sets of multivariate measurement data are now routinely available through automated in-process measurement in many manufacturing industries. These data typically contain valuable information regarding the nature of each major source of process variability. In this article we assume that each variation source causes a distinct spatial variation pattern in the measurement data. The model that we use to represent the variation patterns is of identical structure to one widely used in the so-called “blind source separation” problem that arises in many sensor-array signal processing applications. We argue that methods developed for blind source separation can be used to identify spatial variation patterns in manufacturing data. We also discuss basic blind source separation concepts and their applicability to diagnosing manufacturing variation.     

Pairwise Estimation of Multivariate Gaussian Process Models With Replicated Observations: Application to Multivariate Profile Monitoring

Profile monitoring is often conducted when the product quality is characterized by profiles. Although existing methods almost exclusively deal with univariate profiles, observations of multivariate profile data are increasingly encountered in practice. These data are seldom analyzed in the area of statistical process control due to lack of effective modeling tools. In this article, we propose to analyze them using the multivariate Gaussian process model, which offers a natural way to accommodate both within-profile and between-profile correlations. To mitigate the prohibitively high computation in building such models, a pairwise estimation strategy is adopted. Asymptotic normality of the parameter estimates from this approach has been established. Comprehensive simulation studies are conducted. In the case study, the method has been demonstrated using transmittance profiles from low-emittance glass. Supplementary materials for this article are available online.     

Thresholded Multivariate Principal Component Analysis for Phase I Multichannel Profile Monitoring

Monitoring multichannel profiles has important applications in manufacturing systems improvement, but it is nontrivial to develop efficient statistical methods because profiles are high-dimensional functional data with intrinsic inner- and interchannel correlations, and that the change might only affect a few unknown features of multichannel profiles. To tackle these challenges, we propose a novel thresholded multivariate principal component analysis (PCA) method for multichannel profile monitoring. Our proposed method consists of two steps of dimension reduction: It first applies the functional PCA to extract a reasonably large number of features under the in-control state, and then uses the soft-thresholding techniques to further select significant features capturing profile information under the out-of-control state. The choice of tuning parameter for soft-thresholding is provided based on asymptotic analysis, and extensive numerical studies are conducted to illustrate the efficacy of our proposed thresholded PCA methodology.     

Alternatives to the Multivariate Control Chart for Process Dispersion

In this article, we compare the performances of six new multivariate control chart schemes for process dispersion to the standard multivariate process dispersion control chart. The six new schemes are designed by transforming the standard multivariate control chart statistic for process dispersion into a standard scale so that runs rules can be incorporated into these schemes. This article discusses a simple extension for using runs rules in a multivariate control chart for process dispersion. The extension is deemed important since the use of runs rules is always confined to univariate control charts only. The performances of the six control chart schemes together with the standard control chart are based on the computed average run length (ARL) profiles. Five of the six schemes have shown better ARL performances than the standard multivariate process dispersion control chart.     

Statistical Monitoring of a Sealing Process by Means of Multivariate Accelerometer Data

A multivariate control charting procedure is applied to on-line seal quality evaluation of a packaging process by means of an accelerometer. Based on physical insight it is elucidated in a first step which information in the raw accelerometer data are relevant with respect to the goal of detecting bad seals. Next, a principal component analysis (PCA) based processing of this multivariate information is performed and the related Hotelling's T² and Q test statistics are calculated for further data representation. In a last step proper control charts based on these statistics are used as a process monitoring tool for on-line distinction between good and bad seals. The obtained results show that a correct monitoring of accelerometer signals can be a useful tool for the on-line detection of 'bad seals' in a packaging process.     

Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

In modern manufacturing processes, massive amounts of multivariate data are routinely collected through automated in-process sensing. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio and missing values. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments. This article discusses these issues and advocates the use of multivariate statistical process control based on principal component analysis (MSPC-PCA) as an efficient statistical tool for process understanding, monitoring and diagnosing assignable causes for special events in these contexts. Data from an autobody assembly process are used to illustrate the practical benefits of using MSPC-PCA rather than conventional SPC in manufacturing processes.     

统计过程控制在微电脑控制器生产中的应用:案例研究

以某电子集团生产的某类型控制器的实际生产为例,采用因果分析法对其生产过程中造成质量缺陷的原因从5M1E(人、机、料、法、测、环)方面进行综合分析.通过对其测量系统进行监控,在其稳定可靠的情况下采集数据.运用SPC技术对波峰焊工序进行控制图监控,对失控原因进行分析并在线调整.有效地保证了控制器的质量,为其质量改善指明了方向.     

流程程序分析方法在印染生产线改进中的应用

以某公司的印染生产线为研究对象,运用流程程序分析方法和5W1H提问技术分析生产线存在的问题,并根据ECRS四大原则,对生产线的工艺内容、工艺方法、工艺程序和空间布置提出改进方案。     

面向半导体制造业的统计过程分析与控制

传统的统计过程控制方法不能完全适应半导体制造业生产形式需要。本文在分析半导体光电封装制造模式的特点和实施过程控制所面临的问题的基础上,提出一种基于聚类分析的统计质量控制方法。通过实证分析,证实了该方法的可操作性并取得了良好的实际效果。     

冲截模CAD系统中的工艺性分析

详细论述了作者所研究开发的冲截模ＣＡＤ系统工艺性分析的基本内容，介绍了各类工艺性判别，检查了类型模型的建立，程序流程及技术要点。     

Process capability analysis in the presence of autocorrelation

Process capability analysis when observations are autocorrelated is addressed using time series modelling and regression analysis. Through the use of a numerical example, it is shown that the variance estimate obtained from the original data is no longer an appropriate estimate to be considered for conducting process capability analyses. Copyright © 2002 John Wiley & Sons, Ltd.     

A Method for Determining Equivalence in Industrial Settings: Defining and Testing the Equivalence of Two Methods or Two Laboratories

A common practice in industrial settings is to determine the equivalence between two methods or two laboratories. Average equivalence is the preferred method. Average equivalence focuses only on the comparison of the means. However, it may be also of interest to compare the variabilities. We propose an equivalence method that considers the means and the variabilities when comparing two methods or two laboratories. We develop bounds for conducting statistical tests using the proposed equivalence criterion. Simulation is conducted to study the performance of the bounds. The criteria are the ability to maintain the stated confidence level, as well as the stated test size, and the simulated power of the tests using these bounds. Bounds that perform well for small sample size are also desirable.     

乳化炸药连续乳化设计中的预先危险性分析

在乳化炸药连续乳化工艺设计过程中，应用预先危险性分析，以选择合理的工艺，制定相应的安全防范措施，能起到防患于未然的作用。     

AR(1)型自相关过程的过程能力分析方法研究

传统上进行过程能力分析是基于过程观测数据统计独立这一基本假设,而实际工作中,过程数据并不是总能满足彼此统计独立的假设前提.介绍了过程观测值符合AR(1)型自相关模型时,计算过程能力指数的方法.用一个具体案例进行分析,结果表明:数据间存在正相关时,传统方法高估了过程能力指数,而利用笔者介绍的方法可以得到正确的结论.     

Statistical Validation of Reproducibility of HPLC Peptide Mapping for the Identity of an Investigational Drug Compound Based on Principal Component Analysis

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable “fingerprint” for identity testing and process monitoring. We recently conducted a full method validation study of an optimized reverse-phase high-performance liquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monoclonal antibody. We have used this method routinely for over a year to test production lots for clinical trials and to support bioprocess development. One of the difficulties in the validation of the peptide mapping method is the lack of proper quantitative measures of its reproducibility. A reproducibility study may include method and system precision study, ruggedness study, and robustness study. In this paper, we discuss the use of principal component analysis (PCA) to quantitate peptide maps properly using its projected scores on the reduced dimensions. This approach allowed us not only to summarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility validation process.     

Speckle Interferometry: A Review of the Principal Methods in Use for Experimental Mechanics Applications

Abstract:  With its nearly 40 years of existence, speckle interferometry (SI) has become a complete technique, widely used in many branches of experimental mechanics. It is thus a challenging task to try to summarise in a couple of pages its principal characteristics from both theoretical and practical points of view. Admittedly, even though this goal is not met here, it appeared worth attempting to provide the photomechanics community with a discussion of the ins and outs of the technique. The necessity of a vocabulary free of ambiguity was a prerequisite, and hence the first section is a plea for a clearer definition of the discipline. Moreover, this section offers the opportunity to re-examine the basic aspects of SI. Then, the main features of the method are briefly considered following a strengths, weaknesses, opportunities and threats (SWOT) analysis. Endowed with a lot of specific advantages, compared with other whole-field methods, SI can play an increasing role in photomechanics.     

Utility of Multivariate Analysis in Modeling the Effects of Raw Material Properties and Operating Parameters on Granule and Ribbon Properties Prepared in Roller Compaction

This article aimed to model the effects of raw material properties and roller compactor operating parameters (OPs) on the properties of roller compacted ribbons and granules with the aid of principal component analysis (PCA) and partial least squares (PLS) projection. A database of raw material properties was established through extensive physical and mechanical characterization of several microcrystalline cellulose (MCC) and lactose grades and their blends. A design of experiment (DoE) was used for ribbon production. PLS models constructed with only OP-modeled roller compaction (RC) responded poorly. Inclusion of raw material properties markedly improved the goodness of fit (R²?=?.897) and model predictability (Q²?=?0.72).     

对磁光存储系统中频率法抑制噪声能力的分析

目前在磁光存储系统中,用于探测磁光信号的振幅法对系统中的振幅噪声和极化噪声均很敏感。通过本文详细的理论分析、计算机模拟,得知新型磁光信号探测法——频率法具有较强的抑制噪声的能力。该方法为研制结构简单,读出信号质量好,数据传输速率高的光学头提供了有效的途径。